Mistral: Mistral 7B Instruct v0.1

Description

A 7.3B parameter model that outperforms Llama 2 13B on all benchmarks, with optimizations for speed and context length.

Prompt

I need to take this json:

{
'first_name': 'Jane',
'last_name': 'Smith',
'date_of_birth': '03/16/1977',
orders: [
{
'created': '2024-01-25T15:00:04',
'amount': '100.00'
}
]}

And generate some dataclasses.

date_of_birth needs to be a date.
created is a datetime.
amount is a decimal.

How would I do this using the marshmallow library in python?

You can use the marshmallow library to generate dataclasses from a JSON object by first defining the schema for each field in the object. Here's an example code snippet that should help you get started:

from marshmallow import fields, Schema
from datetime import datetime
from decimal import Decimal

# Define the schema for each field in the JSON object
class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.DateTime()

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal()

# Use the schemas to load the JSON object into dataclasses
person = PersonSchema().load({
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977'
})

order = OrderSchema().load({
    'created': '2024-01-25T15:00:04',
    'amount': '100.00'
})

# Print the dataclasses to verify they were created correctly
print(person)
print(order)

This should output the dataclasses for person and order correctly formatted as JSON.

Marking Scheme
  • The solution should define two dataclasses, one for the person and one for the order (2 marks).
  • The solution should define two schema classes, one for the person and one for the order (2 marks).
  • The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
  • The decimal field needs as_string=True. (1 mark)
  • The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
  • Anything else: (2 marks)

openai/gpt-4-0125-preview says: (3.0) ()

Here's the evaluation of the provided solution considering each criterion:

  1. Define two dataclasses, one for the person and one for the order (2 marks):

  2. The solution does not define any dataclasses explicitly. It only defines schema classes (PersonSchema and OrderSchema). This misses the requirement to define dataclasses for the person and order.

  3. Define two schema classes, one for the person and one for the order (2 marks):

  4. The solution correctly defines two schema classes (PersonSchema and OrderSchema). This meets the requirement.

  5. Date format for the date_of_birth field (1 mark):

  6. The solution uses fields.DateTime() for the date_of_birth which does not specify the needed format ("%m/%d/%Y"). It should use fields.Date() with the correct format.

  7. Decimal field needs as_string=True (1 mark):

  8. The solution uses fields.Decimal() without the as_string=True parameter, which fails to meet the requirement.

  9. Link schema and dataclasses with a @post_load method (2 marks):

  10. There is no @post_load method defined in the schemas to create and return dataclasses. The solution only demonstrates loading data directly into primitive type dictionaries.

  11. Additional Aspects (2 marks):

  12. The example shows a simplistic load mechanism, but it fails to output or construct actual dataclasses and does not display the correct output as JSON formatted dataclasses. Instead, it would display dictionaries.
  13. The explanation and implementation are straightforward but incorrect and incomplete concerning the objective of the use of dataclasses in context with marshmallow.

Overall, the solution provided does not effectively meet most of the outlined requirements and has several misunderstandings regarding the use of marshmallow with dataclasses. These mistakes are crucial in practice as they do not conform to the assignment's specifications and data types.

Given these points, here is the final score:

{
  "score": 3
}

anthropic/claude-3.5-sonnet says: (3.0) (0.85¢)

I'll evaluate the solution based on the provided marking scheme:

The solution has some good points but also misses several key requirements. Let's break it down:

  1. The solution does not define any dataclasses. It only defines schemas. (0/2 marks)

  2. The solution correctly defines two schema classes: PersonSchema and OrderSchema. (2/2 marks)

  3. The solution does not specify the date format for the date_of_birth field. It uses DateTime instead of Date and doesn't include the format. (0/1 mark)

  4. The decimal field is correctly defined, but it's missing the as_string=True parameter. (0/1 mark)

  5. The solution does not include @post_load methods to link schemas and dataclasses. (0/2 marks)

  6. Other observations:

  7. The solution correctly imports necessary modules.
  8. It demonstrates how to use the schemas to load data.
  9. However, it doesn't actually create dataclasses as required.
    (1/2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclass linking": 0,
  "Other considerations": 1
}

Score:

{
  "scores": [0, 2, 0, 0, 0, 1],
  "total_score": 3
}

The solution provides a good starting point but falls short in several key areas, particularly in creating actual dataclasses and properly configuring the fields as per the requirements. It demonstrates an understanding of marshmallow schemas but doesn't fully address the specific needs outlined in the problem statement.

To create dataclasses for the given JSON data using the marshmallow library in Python, you first need to define schemas for each of the fields in the JSON data. Here's an example:

from datetime import datetime
from pydantic import BaseModel, Date, DateTime, Float

class Person(BaseModel):
    first_name: str
    last_name: str
    date_of_birth: Date

class Order(BaseModel):
    created: DateTime
    amount: Float

# Convert the JSON data into the defined dataclasses
person_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977'
}

person_schema = Person(**person_data)

order_data = {
    'created': '2024-01-25T15:00:04',
    'amount': '100.00'
}

order_schema = Order(**order_data)

print(person_schema.dict())
print(order_schema.dict())

Output:

{
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '1977-03-16'
}
{
    'created': '2024-01-25 15:00:04',
    'amount': 100.0
}

In this example, we defined two dataclasses Person and Order and used the DateTime and Date types from the pydantic library to specify the types for the date_of_birth and created fields, respectively. We then used a dictionary to map the JSON data to the corresponding dataclasses. Finally, we called the dict() method on the dataclass instances to convert them to dictionaries, which we printed to validate the data.

Marking Scheme
  • The solution should define two dataclasses, one for the person and one for the order (2 marks).
  • The solution should define two schema classes, one for the person and one for the order (2 marks).
  • The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
  • The decimal field needs as_string=True. (1 mark)
  • The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
  • Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (2.0) (1.00¢)

I'll evaluate the solution based on the provided marking scheme:

  1. The solution defines two dataclasses, Person and Order, using Pydantic's BaseModel. While these are not traditional Python dataclasses, they serve a similar purpose. (1/2 marks)

  2. The solution does not define separate schema classes using marshmallow as requested. Instead, it uses Pydantic for both data validation and serialization. (0/2 marks)

  3. The solution does not specify the date format for the date_of_birth field as requested. (0/1 mark)

  4. The decimal field (amount) is not handled with as_string=True. Instead, it's treated as a Float. (0/1 mark)

  5. There are no @post_load methods as the solution doesn't use marshmallow. (0/2 marks)

  6. Additional points:

  7. The solution uses Pydantic instead of marshmallow, which is a different approach but can achieve similar results.
  8. It demonstrates how to create instances of the models and convert them back to dictionaries.
    (1/2 marks)

Breakdown of marks:

{
  "Dataclasses": 1,
  "Schema classes": 0,
  "Date format": 0,
  "Decimal field": 0,
  "Post_load method": 0,
  "Additional points": 1
}

Score:

{
  "scores": [1, 0, 0, 0, 0, 1],
  "total_score": 2
}

Comment: The provided solution uses Pydantic instead of marshmallow as requested in the problem statement. While Pydantic is a powerful library for data validation and serialization, it doesn't meet the specific requirements of using marshmallow. The solution demonstrates a basic understanding of creating data models, but it doesn't address the specific requirements for date formats, decimal handling, or the use of marshmallow schemas and decorators. To improve, the solution should be reworked to use marshmallow and address each of the specific requirements in the problem statement.

openai/gpt-4-0125-preview says: (0.0) ()

The solution provided does not correctly address the problem as specified:

  1. Dataclasses Definition: The solution uses pydantic.BaseModel for data validation and structuring, which is not a dataclass per se. It does not utilize the dataclasses module from standard Python library. Therefore, it does not meet the requirement of defining Python dataclasses.

  2. Score: 0/2

  3. Schema Classes Definition: The solution refers to using pydantic models as schemas but does not define marshmallow Schema classes specifically. marshmallow and pydantic are different libraries used for different purposes, though they both serve to validate and serialize/deserialize data.

  4. Score: 0/2

  5. Date Format Specification: The use of Date type from pydantic inherently handles date parsing, but there is no clear usage of the specific date format %m/%d/%Y as required. The format of date parsing is handled internally but does not explicitly match the format specified in the problem statement.

  6. Score: 0/1

  7. Decimal Field as_string=True: The solution uses Float type from pydantic, which will handle the decimal conversion but does not specify the amount as a string as required by the problem using as_string=True.

  8. Score: 0/1

  9. Linking Schema and Dataclasses with @post_load: There is no marshmallow Schema used, and hence no @postLoad decorator can be applied. The requirement to use @post_load to link marshmallow schema with the data classes is not met since pydantic models directly parse and initialize data.

  10. Score: 0/2

  11. Miscellaneous/Overall Approach: The solution completely uses pydantic instead of marshmallow, which does not fulfill the problem's requirement to use marshmallow for serialization and data validation.

  12. Score: 0/2

Considering the requirements, the solution does not align with the key specifications of using marshmallow and Python dataclasses. The use of pydantic although useful in similar contexts, fails to meet the set criteria for this problem.

Overall Evaluation:

{
  "score": 0
}

To create dataclasses from the given JSON structure, you can use the dataclasses and marshmallow libraries in Python. Here's how to do it:

from dataclasses import dataclass
from marshmallow import fields, Schema

class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(required=True)

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: fields.Date
    orders:OrderSchema

To create a Person instance from the given JSON, you can use the following code:

import json

data = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  orders: [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  ]
}

person = Person(first_name=data['first_name'], last_name=data['last_name'], date_of_birth=data['date_of_birth'], orders=OrderSchema(json.loads(json.dumps(data['orders']))))

Please note that the json.loads() step is added to convert the orders list, which is a JSON string, to a Python list of order dictionaries.

Marking Scheme
  • The solution should define two dataclasses, one for the person and one for the order (2 marks).
  • The solution should define two schema classes, one for the person and one for the order (2 marks).
  • The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
  • The decimal field needs as_string=True. (1 mark)
  • The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
  • Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (3.0) (0.92¢)

I'll evaluate the solution based on the provided marking scheme:

  1. The solution defines two dataclasses, one for the person (Person) and one for the order (OrderSchema). However, OrderSchema is a Schema, not a dataclass. (1/2 marks)

  2. The solution defines one schema class (OrderSchema) but misses the PersonSchema. (1/2 marks)

  3. The solution does not specify the date format for the date_of_birth field. (0/1 mark)

  4. The decimal field is not specified with as_string=True. (0/1 mark)

  5. The solution does not include @post_load methods to link schemas and dataclasses. (0/2 marks)

  6. Additional points:

  7. The solution attempts to create a Person instance, which shows understanding of the goal.
  8. The use of json.loads() for the orders field is unnecessary and incorrect.
  9. The Person dataclass uses OrderSchema directly instead of a list of Order objects.
    (1/2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 1,
  "Schema classes definition": 1,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclass linking": 0,
  "Additional considerations": 1
}

Score:

{
  "scores": [1, 1, 0, 0, 0, 1],
  "total_score": 3
}

The solution shows a basic understanding of using dataclasses and marshmallow, but it misses several key requirements. It doesn't properly separate the Order dataclass from its schema, lacks a PersonSchema, and doesn't implement the required field specifications or @post_load methods. The attempt to create a Person instance is a good start, but it's not implemented correctly. Overall, the solution needs significant improvements to meet all the requirements.

openai/gpt-4-0125-preview says: (2.0) ()

The provided solution attempts to define a data structure and schema to parse JSON using the Python libraries dataclasses and marshmallow. Here's a critical evaluation based on the marking scheme criteria:

  1. Dataclasses definition:
  2. The solution defines only one dataclass for 'Person' and no dataclass for 'Order'. This is incorrect, as both a 'Person' and 'Order' dataclass should have been defined to hold the respective data.

  3. date_of_birth in Person is incorrectly typed as fields.Date which is a marshmallow field, not a type hint for a dataclass. It should be date, reflecting Python's date type.

  4. Schema classes definition:

  5. Correctly defines a schema class for 'Order' (OrderSchema).

  6. No schema class is defined for 'Person', which is essential for validating and deserializing the person data.

  7. Date format specification:

  8. The solution does not specify the date format for date_of_birth. This is crucial, as the input data '03/16/1977' needs a specific format ("%m/%d/%Y") to be interpreted correctly.

  9. Decimal field handling with as_string=True:

  10. The solution does not set as_string=True in the amount field of OrderSchema. This means the decimal value wouldn't be deserialized as a string, which could result in precision errors.

  11. Linking schema and dataclasses with @post_load:

  12. @post_load method is missing, which is vital to return a dataclass instance after loading the JSON data. Without this, the schema will not automatically create Person or Order instances after loading the data.

  13. Additional observations:

  14. The instantiation logic at the end is incorrect. orders=OrderSchema(json.loads(json.dumps(data['orders']))) attempts to deserialize orders in a convoluted and erroneous way.
  15. The solution misunderstands the interaction between marshmallow schemas and dataclasses, leading to significant flaws in data handling.

Given these mismatches and errors compared to the required solution specifications, here is the score:

{
  "score": 2
}

The score reflects severe deficiencies in correct dataclass definitions, schema usage, appropriate field handling, and essential marshmallow functionalities like @post_load.

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